Pylateasy

This is a brief usage tutorial. Lattieceaasy can be run by calling Run(params) where params is a dictionary of parameter values. This returns a run object.

import pylateasy as pe

params = dict(tf=500., kcutoff=10., N=256, checkpoint_interval=.5)
run = pe.Run(params)

Running LATTICEEASY in 2D with N=256, NDIMS=2, checkpoint_interval=0.5, kcutoff=10.0, tf=500.0, ...

Field evolution can be quickly plotted with .plot() method.

run.plot()

Means and variances are stored in corresponding attributes as pandas dataframes.

run.means.head()

	t_pr	phi	chi
0	0.00	1.714534	4.219693e-17
1	0.04	1.713497	3.631969e-17
2	0.08	1.710345	3.058354e-16
3	0.12	1.705013	7.384405e-16
4	0.16	1.697449	8.701842e-16

run.variances.head()

	t_pr	phi2	chi2
0	0.00	0.000000e+00	1.125129e-13
1	0.04	-2.268775e-08	1.130338e-13
2	0.08	-2.624908e-08	1.162312e-13
3	0.12	-6.045760e-08	1.159374e-13
4	0.16	3.434144e-08	1.134350e-13

Energies

Energy densities are stored in energies attribute and can be plotted with .energies.plot() method.

run.energies.head()

	t_pr	phi_kinetic	chi_kinetic	phi_gradient	chi_gradient	potential_1	potential_2
0	0.000000	0.240136	7.353299e-12	2.327589e-07	1.095314e-12	0.250000	5.741132e-12
1	0.520000	0.405281	6.626339e-12	-5.625328e-08	1.223171e-12	0.157221	5.034282e-12
2	1.040000	0.416293	4.875725e-12	-4.329187e-08	1.624370e-12	0.030403	3.041589e-12
3	1.559999	0.327447	3.321233e-12	4.685298e-09	2.330434e-12	0.000491	5.941639e-13
4	2.079999	0.254840	2.806168e-12	2.955964e-10	2.773799e-12	0.000271	5.506463e-13

run.energies.plot()

Spectra

Occupation number spectra are stored in the spectra attribute. $n_k$ for $\phi$ and $\chi$ respectively are found in spectra.nk_phi and spectra.nk_chi as pandas pivot tables. Spectra can be plotted with spectra.plot().

run.spectra.plot()

Slices

Lattice slices (assuming 2D) are stored in the slices attribute and can be plotted with slices.plot(t) where t is the $t$th slice. By default returns the last slice.

run.slices.plot(10)